docstr: Train the model with train data.
  
  :param data: train data.
  :param epochs: number of epochs to train.
  :param batch_size: total batch size for each iteration. Default: 32.
  :param feature_cols: feature column names if train data is Spark DataFrame.
  :param label_cols: label column names if train data is Spark DataFrame.
  :param validation_data: validation data. Validation data type should be the same
  as train data.
  :param checkpoint_trigger: when to trigger checkpoint during training.
  Should be a zoo.orca.learn.trigger, like EveryEpoch(), SeveralIteration(num_iterations),etc.
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code: def fit(
      self,
      data,
      epochs,
      batch_size=32,
      feature_cols=None,
      label_cols=None,
      validation_data=None,
      checkpoint_trigger=None,
  ):
      """
      Train the model with train data.
  
      :param data: train data.
      :param epochs: number of epochs to train.
      :param batch_size: total batch size for each iteration. Default: 32.
      :param feature_cols: feature column names if train data is Spark DataFrame.
      :param label_cols: label column names if train data is Spark DataFrame.
      :param validation_data: validation data. Validation data type should be the same
      as train data.
      :param checkpoint_trigger: when to trigger checkpoint during training.
      Should be a zoo.orca.learn.trigger, like EveryEpoch(), SeveralIteration(num_iterations),etc.
      """
      raise NotImplementedError
  
file_path: analytics-zoo/pyzoo/zoo/orca/learn/spark_estimator.py
imports: from abc import abstractmethod
  from zoo.orca.learn.base_estimator import BaseEstimator
label_desc: Use image node_id 1 for calling the Estimator obj's underlying member method code with example usage: obj.fit(data, epochs, batch_size, feature_cols, label_cols, validation_data, checkpoint_trigger) without return types